The hidden simplicity of metabolic networks is revealed by multireaction dependencies

Sucrose-driven carbon redox rebalancing eliminates the Crabtree effect and boosts energy metabolism in yeast

Saccharomyces cerevisiae primarily generates energy through glycolysis and respiration. However, the manifestation of the Crabtree effect results in substantial carbon loss and energy inefficiency, which significantly diminishes product yield and escalates substrate costs in microbial cell factories. To address this challenge, we introduce the sucrose phosphorolysis pathway and delete the phosphoglucose isomerase gene PGI1, effectively decoupling glycolysis from respiration and facilitating the metabolic transition of yeast to a Crabtree-negative state. Additionally, a synthetic energy system is engineered to regulate the NADH/NAD+ ratio, ensuring sufficient ATP supply and maintaining redox balance for optimal growth. The reprogrammed yeast strain exhibits significantly higher yields of various non-ethanol compounds, with lactic acid and 3-hydroxypropionic acid production increasing by 8- to 11-fold comparing to the conventional Crabtree-positive strain. This study describes an approach for overcoming the Crabtree effect in yeast, substantially improving energy metabolism, carbon recovery, and product yields.

Kinetic modules are sources of concentration robustness in biochemical networks

Modules represent fundamental building blocks of cellular networks and are thought to facilitate robustness of phenotypes against perturbations. While reaction kinetic shapes the concentration of components and reaction rates, its use in identification of modules entails knowledge of parameter values. Here, we demonstrate that kinetic modules can be efficiently identified on the basis of steady-state reaction rate couplings in large-scale biochemical networks endowed with mass action kinetics without knowledge of parameter values. We then link the kinetic modules of metabolic networks with robustness of metabolite concentrations to perturbations. Analyzing 34 metabolic network models of 26 organisms, we demonstrate that the ordered binding enzyme mechanism leads to increased concentration robustness compared to random binding. Our findings pave the way for usage of modules in synthetic biology and biotechnological applications.

Flux-sum coupling analysis of metabolic network models

Metabolites acting as substrates and regulators of all biochemical reactions play an important role in maintaining the functionality of cellular metabolism. Despite advances in the constraint-based framework for genome-scale metabolic modeling, we lack reliable proxies for metabolite concentrations that can be efficiently determined and that allow us to investigate the relationship between metabolite concentrations in specific metabolic states in the absence of measurements. Here, we introduce a constraint-based approach, the flux-sum coupling analysis (FSCA), which facilitates the study of the interdependencies between metabolite concentrations by determining coupling relationships based on the flux-sum of metabolites. Application of FSCA on metabolic models of Escherichia coli, Saccharomyces cerevisiae, and Arabidopsis thaliana showed that the three coupling relationships are present in all models and pinpointed similarities in coupled metabolite pairs. Using the available concentration measurements of E. coli metabolites, we demonstrated that the coupling relationships identified by FSCA can capture the qualitative associations between metabolite concentrations and that flux-sum is a reliable proxy for metabolite concentration. Therefore, FSCA provides a novel tool for exploring and understanding the intricate interdependencies between the metabolite concentrations, advancing the understanding of metabolic regulation, and improving flux-centered systems biology approaches.

Maximizing multi-reaction dependencies provides more accurate and precise predictions of intracellular fluxes than the principle of parsimony

Intracellular fluxes represent a joint outcome of cellular transcription and translation and reflect the availability and usage of nutrients from the environment. While approaches from the constraint-based metabolic framework can accurately predict cellular phenotypes, such as growth and exchange rates with the environment, accurate prediction of intracellular fluxes remains a pressing problem. Parsimonious flux balance analysis (pFBA) has become an approach of choice to predict intracellular fluxes by employing the principle of efficient usage of protein resources. Nevertheless, comparative analyses of intracellular flux predictions from pFBA against fluxes estimated from labeling experiments remain scarce. Here, we posited that steady-state flux distributions derived from the principle of maximizing multi-reaction dependencies are of improved accuracy and precision than those resulting from pFBA. To this end, we designed a constraint-based approach, termed complex-balanced FBA (cbFBA), to predict steady-state flux distributions that support the given specific growth rate and exchange fluxes. We showed that the steady-state flux distributions resulting from cbFBA in comparison to pFBA show better agreement with experimentally measured fluxes from 17 Escherichia coli strains and are more precise, due to the smaller space of alternative solutions. We also showed that the same principle holds in eukaryotes by comparing the predictions of pFBA and cbFBA against experimentally derived steady-state flux distributions from 26 knock-out mutants of Saccharomyces cerevisiae. Furthermore, our results showed that intracellular fluxes predicted by cbFBA provide better support for the principle of minimizing metabolic adjustment between mutants and wild types. Together, our findings point that other principles that consider the dynamics and coordination of steady states may govern the distribution of intracellular fluxes.